Overview

Dataset statistics

Number of variables20
Number of observations3333
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory498.1 KiB
Average record size in memory153.0 B

Variable types

Categorical2
Numeric15
Boolean3

Alerts

State has a high cardinality: 51 distinct valuesHigh cardinality
Number vmail messages is highly overall correlated with Voice mail planHigh correlation
Total day minutes is highly overall correlated with Total day chargeHigh correlation
Total day charge is highly overall correlated with Total day minutesHigh correlation
Total eve minutes is highly overall correlated with Total eve chargeHigh correlation
Total eve charge is highly overall correlated with Total eve minutesHigh correlation
Total night minutes is highly overall correlated with Total night chargeHigh correlation
Total night charge is highly overall correlated with Total night minutesHigh correlation
Total intl minutes is highly overall correlated with Total intl chargeHigh correlation
Total intl charge is highly overall correlated with Total intl minutesHigh correlation
Voice mail plan is highly overall correlated with Number vmail messagesHigh correlation
International plan is highly imbalanced (54.1%)Imbalance
Number vmail messages has 2411 (72.3%) zerosZeros
Customer service calls has 697 (20.9%) zerosZeros

Reproduction

Analysis started2023-03-26 14:22:53.551294
Analysis finished2023-03-26 14:23:18.645553
Duration25.09 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

State
Categorical

Distinct51
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
WV
 
106
MN
 
84
NY
 
83
AL
 
80
WI
 
78
Other values (46)
2902 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters6666
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK

Common Values

ValueCountFrequency (%)
WV 106
 
3.2%
MN 84
 
2.5%
NY 83
 
2.5%
AL 80
 
2.4%
WI 78
 
2.3%
OH 78
 
2.3%
OR 78
 
2.3%
WY 77
 
2.3%
VA 77
 
2.3%
CT 74
 
2.2%
Other values (41) 2518
75.5%

Length

2023-03-26T19:53:18.723002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 106
 
3.2%
mn 84
 
2.5%
ny 83
 
2.5%
al 80
 
2.4%
wi 78
 
2.3%
oh 78
 
2.3%
or 78
 
2.3%
wy 77
 
2.3%
va 77
 
2.3%
ct 74
 
2.2%
Other values (41) 2518
75.5%

Most occurring characters

ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 6666
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 6666
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6666
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 734
 
11.0%
A 687
 
10.3%
M 612
 
9.2%
I 515
 
7.7%
T 412
 
6.2%
D 380
 
5.7%
C 356
 
5.3%
O 346
 
5.2%
W 327
 
4.9%
V 322
 
4.8%
Other values (14) 1975
29.6%

Account length
Real number (ℝ)

Distinct212
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.06481
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:18.826719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q174
median101
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)53

Descriptive statistics

Standard deviation39.822106
Coefficient of variation (CV)0.39402545
Kurtosis-0.10783598
Mean101.06481
Median Absolute Deviation (MAD)27
Skewness0.096606294
Sum336849
Variance1585.8001
MonotonicityNot monotonic
2023-03-26T19:53:18.943542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 43
 
1.3%
87 42
 
1.3%
101 40
 
1.2%
93 40
 
1.2%
90 39
 
1.2%
95 38
 
1.1%
86 38
 
1.1%
100 37
 
1.1%
116 37
 
1.1%
112 36
 
1.1%
Other values (202) 2943
88.3%
ValueCountFrequency (%)
1 8
0.2%
2 1
 
< 0.1%
3 5
0.2%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 2
 
0.1%
7 2
 
0.1%
8 1
 
< 0.1%
9 3
 
0.1%
10 3
 
0.1%
ValueCountFrequency (%)
243 1
 
< 0.1%
232 1
 
< 0.1%
225 2
0.1%
224 2
0.1%
221 1
 
< 0.1%
217 2
0.1%
215 1
 
< 0.1%
212 2
0.1%
210 2
0.1%
209 3
0.1%

Area code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
415
1655 
510
840 
408
838 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9999
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415
2nd row415
3rd row415
4th row408
5th row415

Common Values

ValueCountFrequency (%)
415 1655
49.7%
510 840
25.2%
408 838
25.1%

Length

2023-03-26T19:53:19.048553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-26T19:53:19.155739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
415 1655
49.7%
510 840
25.2%
408 838
25.1%

Most occurring characters

ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9999
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 9999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2495
25.0%
5 2495
25.0%
4 2493
24.9%
0 1678
16.8%
8 838
 
8.4%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
3010 
True
323 
ValueCountFrequency (%)
False 3010
90.3%
True 323
 
9.7%
2023-03-26T19:53:19.241763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
2411 
True
922 
ValueCountFrequency (%)
False 2411
72.3%
True 922
 
27.7%
2023-03-26T19:53:19.323006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Number vmail messages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0990099
Minimum0
Maximum51
Zeros2411
Zeros (%)72.3%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:19.409411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile36
Maximum51
Range51
Interquartile range (IQR)20

Descriptive statistics

Standard deviation13.688365
Coefficient of variation (CV)1.6901282
Kurtosis-0.051128539
Mean8.0990099
Median Absolute Deviation (MAD)0
Skewness1.2648236
Sum26994
Variance187.37135
MonotonicityNot monotonic
2023-03-26T19:53:19.517979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 2411
72.3%
31 60
 
1.8%
29 53
 
1.6%
28 51
 
1.5%
33 46
 
1.4%
27 44
 
1.3%
30 44
 
1.3%
24 42
 
1.3%
26 41
 
1.2%
32 41
 
1.2%
Other values (36) 500
 
15.0%
ValueCountFrequency (%)
0 2411
72.3%
4 1
 
< 0.1%
8 2
 
0.1%
9 2
 
0.1%
10 1
 
< 0.1%
11 2
 
0.1%
12 6
 
0.2%
13 4
 
0.1%
14 7
 
0.2%
15 9
 
0.3%
ValueCountFrequency (%)
51 1
 
< 0.1%
50 2
 
0.1%
49 1
 
< 0.1%
48 2
 
0.1%
47 3
 
0.1%
46 4
 
0.1%
45 6
 
0.2%
44 7
0.2%
43 9
0.3%
42 15
0.5%

Total day minutes
Real number (ℝ)

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.7751
Minimum0
Maximum350.8
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:19.629902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile89.92
Q1143.7
median179.4
Q3216.4
95-th percentile270.74
Maximum350.8
Range350.8
Interquartile range (IQR)72.7

Descriptive statistics

Standard deviation54.467389
Coefficient of variation (CV)0.30297516
Kurtosis-0.019940379
Mean179.7751
Median Absolute Deviation (MAD)36.3
Skewness-0.029077067
Sum599190.4
Variance2966.6965
MonotonicityNot monotonic
2023-03-26T19:53:19.741932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
154 8
 
0.2%
159.5 8
 
0.2%
174.5 8
 
0.2%
183.4 7
 
0.2%
175.4 7
 
0.2%
162.3 7
 
0.2%
178.7 6
 
0.2%
194.8 6
 
0.2%
189.3 6
 
0.2%
146.3 6
 
0.2%
Other values (1657) 3264
97.9%
ValueCountFrequency (%)
0 2
0.1%
2.6 1
< 0.1%
7.8 1
< 0.1%
7.9 1
< 0.1%
12.5 1
< 0.1%
17.6 1
< 0.1%
18.9 1
< 0.1%
19.5 1
< 0.1%
25.9 1
< 0.1%
27 1
< 0.1%
ValueCountFrequency (%)
350.8 1
< 0.1%
346.8 1
< 0.1%
345.3 1
< 0.1%
337.4 1
< 0.1%
335.5 1
< 0.1%
334.3 1
< 0.1%
332.9 1
< 0.1%
329.8 1
< 0.1%
328.1 1
< 0.1%
326.5 1
< 0.1%

Total day calls
Real number (ℝ)

Distinct119
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.43564
Minimum0
Maximum165
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:19.866829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median101
Q3114
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)27

Descriptive statistics

Standard deviation20.069084
Coefficient of variation (CV)0.19982034
Kurtosis0.24318152
Mean100.43564
Median Absolute Deviation (MAD)13
Skewness-0.11178664
Sum334752
Variance402.76814
MonotonicityNot monotonic
2023-03-26T19:53:19.973655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 78
 
2.3%
105 75
 
2.3%
95 69
 
2.1%
107 69
 
2.1%
104 68
 
2.0%
108 67
 
2.0%
97 67
 
2.0%
106 66
 
2.0%
112 66
 
2.0%
110 66
 
2.0%
Other values (109) 2642
79.3%
ValueCountFrequency (%)
0 2
0.1%
30 1
 
< 0.1%
35 1
 
< 0.1%
36 1
 
< 0.1%
40 2
0.1%
42 2
0.1%
44 3
0.1%
45 3
0.1%
47 2
0.1%
48 3
0.1%
ValueCountFrequency (%)
165 1
 
< 0.1%
163 1
 
< 0.1%
160 1
 
< 0.1%
158 3
0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
152 1
 
< 0.1%
151 5
0.2%
150 6
0.2%
149 1
 
< 0.1%

Total day charge
Real number (ℝ)

Distinct1667
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.562307
Minimum0
Maximum59.64
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:20.091679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.288
Q124.43
median30.5
Q336.79
95-th percentile46.028
Maximum59.64
Range59.64
Interquartile range (IQR)12.36

Descriptive statistics

Standard deviation9.2594346
Coefficient of variation (CV)0.30296909
Kurtosis-0.019811787
Mean30.562307
Median Absolute Deviation (MAD)6.17
Skewness-0.029083268
Sum101864.17
Variance85.737128
MonotonicityNot monotonic
2023-03-26T19:53:20.448410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.18 8
 
0.2%
27.12 8
 
0.2%
29.67 8
 
0.2%
31.18 7
 
0.2%
29.82 7
 
0.2%
27.59 7
 
0.2%
30.38 6
 
0.2%
33.12 6
 
0.2%
32.18 6
 
0.2%
24.87 6
 
0.2%
Other values (1657) 3264
97.9%
ValueCountFrequency (%)
0 2
0.1%
0.44 1
< 0.1%
1.33 1
< 0.1%
1.34 1
< 0.1%
2.13 1
< 0.1%
2.99 1
< 0.1%
3.21 1
< 0.1%
3.32 1
< 0.1%
4.4 1
< 0.1%
4.59 1
< 0.1%
ValueCountFrequency (%)
59.64 1
< 0.1%
58.96 1
< 0.1%
58.7 1
< 0.1%
57.36 1
< 0.1%
57.04 1
< 0.1%
56.83 1
< 0.1%
56.59 1
< 0.1%
56.07 1
< 0.1%
55.78 1
< 0.1%
55.51 1
< 0.1%

Total eve minutes
Real number (ℝ)

Distinct1611
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.98035
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:20.561050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.8
Q1166.6
median201.4
Q3235.3
95-th percentile284.3
Maximum363.7
Range363.7
Interquartile range (IQR)68.7

Descriptive statistics

Standard deviation50.713844
Coefficient of variation (CV)0.25233235
Kurtosis0.025629753
Mean200.98035
Median Absolute Deviation (MAD)34.4
Skewness-0.023877456
Sum669867.5
Variance2571.894
MonotonicityNot monotonic
2023-03-26T19:53:20.666997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
169.9 9
 
0.3%
167.2 7
 
0.2%
180.5 7
 
0.2%
201 7
 
0.2%
161.7 7
 
0.2%
209.4 7
 
0.2%
230.9 7
 
0.2%
220.6 7
 
0.2%
195.5 7
 
0.2%
230 6
 
0.2%
Other values (1601) 3262
97.9%
ValueCountFrequency (%)
0 1
< 0.1%
31.2 1
< 0.1%
42.2 1
< 0.1%
42.5 1
< 0.1%
43.9 1
< 0.1%
48.1 1
< 0.1%
49.2 1
< 0.1%
52.9 1
< 0.1%
56 1
< 0.1%
58.6 1
< 0.1%
ValueCountFrequency (%)
363.7 1
< 0.1%
361.8 1
< 0.1%
354.2 1
< 0.1%
351.6 1
< 0.1%
350.9 1
< 0.1%
350.5 1
< 0.1%
348.5 1
< 0.1%
347.3 1
< 0.1%
341.3 1
< 0.1%
339.9 1
< 0.1%

Total eve calls
Real number (ℝ)

Distinct123
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.11431
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:20.784351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.922625
Coefficient of variation (CV)0.19899877
Kurtosis0.20615647
Mean100.11431
Median Absolute Deviation (MAD)13
Skewness-0.055563139
Sum333681
Variance396.911
MonotonicityNot monotonic
2023-03-26T19:53:20.892530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 80
 
2.4%
94 79
 
2.4%
108 71
 
2.1%
102 70
 
2.1%
97 70
 
2.1%
88 69
 
2.1%
101 68
 
2.0%
109 67
 
2.0%
98 66
 
2.0%
111 65
 
2.0%
Other values (113) 2628
78.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
12 1
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
42 1
 
< 0.1%
43 1
 
< 0.1%
44 1
 
< 0.1%
45 1
 
< 0.1%
46 3
0.1%
48 6
0.2%
ValueCountFrequency (%)
170 1
 
< 0.1%
168 1
 
< 0.1%
164 1
 
< 0.1%
159 1
 
< 0.1%
157 1
 
< 0.1%
156 1
 
< 0.1%
155 3
0.1%
154 2
 
0.1%
153 1
 
< 0.1%
152 6
0.2%

Total eve charge
Real number (ℝ)

Distinct1440
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.08354
Minimum0
Maximum30.91
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:21.009705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10.1
Q114.16
median17.12
Q320
95-th percentile24.17
Maximum30.91
Range30.91
Interquartile range (IQR)5.84

Descriptive statistics

Standard deviation4.3106676
Coefficient of variation (CV)0.25232871
Kurtosis0.025487405
Mean17.08354
Median Absolute Deviation (MAD)2.92
Skewness-0.023857989
Sum56939.44
Variance18.581856
MonotonicityNot monotonic
2023-03-26T19:53:21.117359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.25 11
 
0.3%
16.12 11
 
0.3%
15.9 10
 
0.3%
17.09 9
 
0.3%
18.62 9
 
0.3%
17.99 9
 
0.3%
14.44 9
 
0.3%
18.96 8
 
0.2%
16.35 8
 
0.2%
16.97 8
 
0.2%
Other values (1430) 3241
97.2%
ValueCountFrequency (%)
0 1
< 0.1%
2.65 1
< 0.1%
3.59 1
< 0.1%
3.61 1
< 0.1%
3.73 1
< 0.1%
4.09 1
< 0.1%
4.18 1
< 0.1%
4.5 1
< 0.1%
4.76 1
< 0.1%
4.98 1
< 0.1%
ValueCountFrequency (%)
30.91 1
< 0.1%
30.75 1
< 0.1%
30.11 1
< 0.1%
29.89 1
< 0.1%
29.83 1
< 0.1%
29.79 1
< 0.1%
29.62 1
< 0.1%
29.52 1
< 0.1%
29.01 1
< 0.1%
28.89 1
< 0.1%

Total night minutes
Real number (ℝ)

Distinct1591
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.87204
Minimum23.2
Maximum395
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:21.228829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum23.2
5-th percentile118.18
Q1167
median201.2
Q3235.3
95-th percentile282.84
Maximum395
Range371.8
Interquartile range (IQR)68.3

Descriptive statistics

Standard deviation50.573847
Coefficient of variation (CV)0.25177146
Kurtosis0.085816078
Mean200.87204
Median Absolute Deviation (MAD)34.2
Skewness0.0089212911
Sum669506.5
Variance2557.714
MonotonicityNot monotonic
2023-03-26T19:53:21.337478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
191.4 8
 
0.2%
210 8
 
0.2%
188.2 8
 
0.2%
197.4 8
 
0.2%
214.6 8
 
0.2%
193.6 7
 
0.2%
206.1 7
 
0.2%
194.3 7
 
0.2%
214.7 7
 
0.2%
231.5 7
 
0.2%
Other values (1581) 3258
97.7%
ValueCountFrequency (%)
23.2 1
< 0.1%
43.7 1
< 0.1%
45 1
< 0.1%
47.4 1
< 0.1%
50.1 2
0.1%
53.3 1
< 0.1%
54 1
< 0.1%
54.5 1
< 0.1%
56.6 1
< 0.1%
57.5 1
< 0.1%
ValueCountFrequency (%)
395 1
< 0.1%
381.9 1
< 0.1%
377.5 1
< 0.1%
367.7 1
< 0.1%
364.9 1
< 0.1%
364.3 1
< 0.1%
354.9 1
< 0.1%
352.5 1
< 0.1%
352.2 1
< 0.1%
350.2 1
< 0.1%

Total night calls
Real number (ℝ)

Distinct120
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.10771
Minimum33
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:21.450516image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q187
median100
Q3113
95-th percentile132
Maximum175
Range142
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.568609
Coefficient of variation (CV)0.19547555
Kurtosis-0.072019579
Mean100.10771
Median Absolute Deviation (MAD)13
Skewness0.03249957
Sum333659
Variance382.93047
MonotonicityNot monotonic
2023-03-26T19:53:21.558251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 84
 
2.5%
104 78
 
2.3%
91 76
 
2.3%
102 72
 
2.2%
100 69
 
2.1%
106 69
 
2.1%
98 67
 
2.0%
94 66
 
2.0%
103 65
 
2.0%
95 64
 
1.9%
Other values (110) 2623
78.7%
ValueCountFrequency (%)
33 1
< 0.1%
36 1
< 0.1%
38 1
< 0.1%
42 2
0.1%
44 1
< 0.1%
46 1
< 0.1%
48 1
< 0.1%
49 2
0.1%
50 2
0.1%
51 2
0.1%
ValueCountFrequency (%)
175 1
 
< 0.1%
166 1
 
< 0.1%
164 1
 
< 0.1%
158 1
 
< 0.1%
157 2
0.1%
156 2
0.1%
155 2
0.1%
154 2
0.1%
153 3
0.1%
152 3
0.1%

Total night charge
Real number (ℝ)

Distinct933
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.0393249
Minimum1.04
Maximum17.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:21.675199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.04
5-th percentile5.316
Q17.52
median9.05
Q310.59
95-th percentile12.73
Maximum17.77
Range16.73
Interquartile range (IQR)3.07

Descriptive statistics

Standard deviation2.2758728
Coefficient of variation (CV)0.25177465
Kurtosis0.08566318
Mean9.0393249
Median Absolute Deviation (MAD)1.54
Skewness0.0088862368
Sum30128.07
Variance5.1795972
MonotonicityNot monotonic
2023-03-26T19:53:21.782919image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.66 15
 
0.5%
9.45 15
 
0.5%
8.47 14
 
0.4%
8.88 14
 
0.4%
7.69 13
 
0.4%
8.64 12
 
0.4%
10.8 11
 
0.3%
10.49 11
 
0.3%
10.35 11
 
0.3%
8.57 11
 
0.3%
Other values (923) 3206
96.2%
ValueCountFrequency (%)
1.04 1
< 0.1%
1.97 1
< 0.1%
2.03 1
< 0.1%
2.13 1
< 0.1%
2.25 2
0.1%
2.4 1
< 0.1%
2.43 1
< 0.1%
2.45 1
< 0.1%
2.55 1
< 0.1%
2.59 1
< 0.1%
ValueCountFrequency (%)
17.77 1
< 0.1%
17.19 1
< 0.1%
16.99 1
< 0.1%
16.55 1
< 0.1%
16.42 1
< 0.1%
16.39 1
< 0.1%
15.97 1
< 0.1%
15.86 1
< 0.1%
15.85 1
< 0.1%
15.76 1
< 0.1%

Total intl minutes
Real number (ℝ)

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.237294
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:21.901048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312.1
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation2.7918395
Coefficient of variation (CV)0.27271265
Kurtosis0.60918476
Mean10.237294
Median Absolute Deviation (MAD)1.8
Skewness-0.24513594
Sum34120.9
Variance7.7943681
MonotonicityNot monotonic
2023-03-26T19:53:22.017888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 62
 
1.9%
11.3 59
 
1.8%
9.8 56
 
1.7%
10.9 56
 
1.7%
10.1 53
 
1.6%
10.6 53
 
1.6%
10.2 53
 
1.6%
11 52
 
1.6%
11.1 52
 
1.6%
9.7 51
 
1.5%
Other values (152) 2786
83.6%
ValueCountFrequency (%)
0 18
0.5%
1.1 1
 
< 0.1%
1.3 1
 
< 0.1%
2 2
 
0.1%
2.1 2
 
0.1%
2.2 1
 
< 0.1%
2.4 1
 
< 0.1%
2.5 1
 
< 0.1%
2.6 1
 
< 0.1%
2.7 1
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18.9 1
 
< 0.1%
18.4 1
 
< 0.1%
18.3 1
 
< 0.1%
18.2 2
0.1%
18 3
0.1%
17.9 1
 
< 0.1%
17.8 2
0.1%
17.6 2
0.1%
17.5 3
0.1%

Total intl calls
Real number (ℝ)

Distinct21
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4794479
Minimum0
Maximum20
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:22.128362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4612143
Coefficient of variation (CV)0.54944589
Kurtosis3.083589
Mean4.4794479
Median Absolute Deviation (MAD)1
Skewness1.3214782
Sum14930
Variance6.0575757
MonotonicityNot monotonic
2023-03-26T19:53:22.222590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 668
20.0%
4 619
18.6%
2 489
14.7%
5 472
14.2%
6 336
10.1%
7 218
 
6.5%
1 160
 
4.8%
8 116
 
3.5%
9 109
 
3.3%
10 50
 
1.5%
Other values (11) 96
 
2.9%
ValueCountFrequency (%)
0 18
 
0.5%
1 160
 
4.8%
2 489
14.7%
3 668
20.0%
4 619
18.6%
5 472
14.2%
6 336
10.1%
7 218
 
6.5%
8 116
 
3.5%
9 109
 
3.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 3
 
0.1%
17 1
 
< 0.1%
16 2
 
0.1%
15 7
 
0.2%
14 6
 
0.2%
13 14
0.4%
12 15
0.5%
11 28
0.8%

Total intl charge
Real number (ℝ)

Distinct162
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7645815
Minimum0
Maximum5.4
Zeros18
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:22.330949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.27
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.75377261
Coefficient of variation (CV)0.27265343
Kurtosis0.60961043
Mean2.7645815
Median Absolute Deviation (MAD)0.48
Skewness-0.24528651
Sum9214.35
Variance0.56817315
MonotonicityNot monotonic
2023-03-26T19:53:22.441646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 62
 
1.9%
3.05 59
 
1.8%
2.65 56
 
1.7%
2.94 56
 
1.7%
2.73 53
 
1.6%
2.86 53
 
1.6%
2.75 53
 
1.6%
2.97 52
 
1.6%
3 52
 
1.6%
2.62 51
 
1.5%
Other values (152) 2786
83.6%
ValueCountFrequency (%)
0 18
0.5%
0.3 1
 
< 0.1%
0.35 1
 
< 0.1%
0.54 2
 
0.1%
0.57 2
 
0.1%
0.59 1
 
< 0.1%
0.65 1
 
< 0.1%
0.68 1
 
< 0.1%
0.7 1
 
< 0.1%
0.73 1
 
< 0.1%
ValueCountFrequency (%)
5.4 1
 
< 0.1%
5.1 1
 
< 0.1%
4.97 1
 
< 0.1%
4.94 1
 
< 0.1%
4.91 2
0.1%
4.86 3
0.1%
4.83 1
 
< 0.1%
4.81 2
0.1%
4.75 2
0.1%
4.73 3
0.1%

Customer service calls
Real number (ℝ)

Distinct10
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5628563
Minimum0
Maximum9
Zeros697
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size26.2 KiB
2023-03-26T19:53:22.535583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.315491
Coefficient of variation (CV)0.84172234
Kurtosis1.7309137
Mean1.5628563
Median Absolute Deviation (MAD)1
Skewness1.0913595
Sum5209
Variance1.7305167
MonotonicityNot monotonic
2023-03-26T19:53:22.613730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 1181
35.4%
2 759
22.8%
0 697
20.9%
3 429
 
12.9%
4 166
 
5.0%
5 66
 
2.0%
6 22
 
0.7%
7 9
 
0.3%
9 2
 
0.1%
8 2
 
0.1%
ValueCountFrequency (%)
0 697
20.9%
1 1181
35.4%
2 759
22.8%
3 429
 
12.9%
4 166
 
5.0%
5 66
 
2.0%
6 22
 
0.7%
7 9
 
0.3%
8 2
 
0.1%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 2
 
0.1%
7 9
 
0.3%
6 22
 
0.7%
5 66
 
2.0%
4 166
 
5.0%
3 429
 
12.9%
2 759
22.8%
1 1181
35.4%
0 697
20.9%

Churn
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
False
2850 
True
483 
ValueCountFrequency (%)
False 2850
85.5%
True 483
 
14.5%
2023-03-26T19:53:22.704149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Interactions

2023-03-26T19:53:16.707900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:54.701166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.257225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.944560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.557892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.037674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.478016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.184659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.678105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.133771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.625401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.348739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.841721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.392795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.960081image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.800568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:54.821033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.348774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.082036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.652846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.128078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.572875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.277918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.768292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.225667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.719745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.444211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.939331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.492644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:15.059479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.895577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:54.913968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.438807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.211756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.751184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.219643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.665712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.373393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.860184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.324158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.049589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.538113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.039408image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.590461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:15.421849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.993294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.013712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.704278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.319329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.851567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.316833image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.989296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.479110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.959670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.426239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.151181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.638629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.146070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.695862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:15.523532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.089379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.112093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.799637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.418873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.949330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.410852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.084687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.577942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.056062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.523500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.248011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.734884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.246262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.797355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:15.621445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.183812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.230108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.896685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.519018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.056681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.504853image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.180766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.674261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.152302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.621956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.347198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.833483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.349907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.898178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:15.719125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.283471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.332955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.997089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.623109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.161577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.603126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.279852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.774790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.249448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.723489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.446139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.933119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.454656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.008031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:15.816763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.380151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.431033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.094447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.725397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.258634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.702423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.378745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.872620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.350015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.823884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.548207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.040076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.560996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.113362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:15.917429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.479782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.535268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.195260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.825855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.353112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.797028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.477484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.969241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.444609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.921907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.644234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.138150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.663021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.215523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.016218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.578306image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.645711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.295782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:58.933209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.452065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.896900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.578339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.072962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.544852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.021121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.745744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.242816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.769138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.321883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.116737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.678611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.750597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.395186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.037834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.552320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:01.993761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.679518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.174212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.642920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.125029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.845741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.341091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.875031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.438313image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.219379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.787376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.854282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.493158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.141763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.650274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.091639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.781169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.277735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.742860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.227402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:09.944389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.439855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:12.978521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.546028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.315550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:17.895503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:55.960868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.595027image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.257189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.750785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.195577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.886526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.384625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.846301image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.332404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.053528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.547570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.087923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.657210image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.418530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:18.001504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.068806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.701036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.365844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.853488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.298199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:03.990864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.493828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:06.949999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.438892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.162821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.652123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.195597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.763684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.523089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:18.099222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:56.165160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:57.799510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:52:59.462862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:00.943714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:02.390783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:04.091150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:05.586359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:07.043325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:08.532445image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:10.259092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:11.748387image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:13.296883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:14.865746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-26T19:53:16.618568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-03-26T19:53:22.798028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Account lengthNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsStateArea codeInternational planVoice mail planChurn
Account length1.0000.0030.0180.0330.018-0.0080.018-0.008-0.014-0.008-0.0140.0150.0270.015-0.0060.0000.0110.0150.0000.000
Number vmail messages0.0031.0000.004-0.0120.0040.021-0.0070.0210.0050.0110.005-0.0020.006-0.002-0.0200.0000.0000.0180.9980.108
Total day minutes0.0180.0041.0000.0091.0000.0060.0180.006-0.0060.023-0.006-0.016-0.000-0.016-0.0150.0000.0320.0680.0330.355
Total day calls0.033-0.0120.0091.0000.009-0.0140.014-0.0140.019-0.0180.0190.0150.0040.015-0.0210.0200.0140.0380.0000.048
Total day charge0.0180.0041.0000.0091.0000.0060.0180.006-0.0060.023-0.006-0.016-0.000-0.016-0.0150.0000.0320.0680.0330.355
Total eve minutes-0.0080.0210.006-0.0140.0061.000-0.0101.000-0.0130.003-0.013-0.0030.014-0.003-0.0180.0000.0190.0360.0180.080
Total eve calls0.018-0.0070.0180.0140.018-0.0101.000-0.0100.0050.0060.0050.0000.0150.0000.0030.0460.0000.0000.0000.000
Total eve charge-0.0080.0210.006-0.0140.0061.000-0.0101.000-0.0130.003-0.013-0.0030.014-0.003-0.0180.0000.0130.0340.0180.080
Total night minutes-0.0140.005-0.0060.019-0.006-0.0130.005-0.0131.0000.0091.000-0.0100.000-0.010-0.0130.0000.0070.0320.0340.020
Total night calls-0.0080.0110.023-0.0180.0230.0030.0060.0030.0091.0000.009-0.006-0.003-0.006-0.0080.0150.0310.0000.0000.003
Total night charge-0.0140.005-0.0060.019-0.006-0.0130.005-0.0131.0000.0091.000-0.0100.000-0.010-0.0130.0000.0000.0320.0320.016
Total intl minutes0.015-0.002-0.0160.015-0.016-0.0030.000-0.003-0.010-0.006-0.0101.0000.0181.000-0.0170.0000.0000.0250.0000.056
Total intl calls0.0270.006-0.0000.004-0.0000.0140.0150.0140.000-0.0030.0000.0181.0000.018-0.0010.0000.0000.0260.0000.089
Total intl charge0.015-0.002-0.0160.015-0.016-0.0030.000-0.003-0.010-0.006-0.0101.0000.0181.000-0.0170.0000.0000.0250.0000.056
Customer service calls-0.006-0.020-0.015-0.021-0.015-0.0180.003-0.018-0.013-0.008-0.013-0.017-0.001-0.0171.0000.0000.0220.0430.0180.316
State0.0000.0000.0000.0200.0000.0000.0460.0000.0000.0150.0000.0000.0000.0000.0001.0000.0000.0640.0000.100
Area code0.0110.0000.0320.0140.0320.0190.0000.0130.0070.0310.0000.0000.0000.0000.0220.0001.0000.0420.0000.000
International plan0.0150.0180.0680.0380.0680.0360.0000.0340.0320.0000.0320.0250.0260.0250.0430.0640.0421.0000.0000.258
Voice mail plan0.0000.9980.0330.0000.0330.0180.0000.0180.0340.0000.0320.0000.0000.0000.0180.0000.0000.0001.0000.100
Churn0.0000.1080.3550.0480.3550.0800.0000.0800.0200.0030.0160.0560.0890.0560.3160.1000.0000.2580.1001.000

Missing values

2023-03-26T19:53:18.251942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-26T19:53:18.521269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
0KS128415NoYes25265.111045.07197.49916.78244.79111.0110.032.701False
1OH107415NoYes26161.612327.47195.510316.62254.410311.4513.733.701False
2NJ137415NoNo0243.411441.38121.211010.30162.61047.3212.253.290False
3OH84408YesNo0299.47150.9061.9885.26196.9898.866.671.782False
4OK75415YesNo0166.711328.34148.312212.61186.91218.4110.132.733False
5AL118510YesNo0223.49837.98220.610118.75203.91189.186.361.700False
6MA121510NoYes24218.28837.09348.510829.62212.61189.577.572.033False
7MO147415YesNo0157.07926.69103.1948.76211.8969.537.161.920False
8LA117408NoNo0184.59731.37351.68029.89215.8909.718.742.351False
9WV141415YesYes37258.68443.96222.011118.87326.49714.6911.253.020False
StateAccount lengthArea codeInternational planVoice mail planNumber vmail messagesTotal day minutesTotal day callsTotal day chargeTotal eve minutesTotal eve callsTotal eve chargeTotal night minutesTotal night callsTotal night chargeTotal intl minutesTotal intl callsTotal intl chargeCustomer service callsChurn
3323IN117415NoNo0118.412620.13249.39721.19227.05610.2213.633.675True
3324WV159415NoNo0169.811428.87197.710516.80193.7828.7211.643.131False
3325OH78408NoNo0193.49932.88116.9889.94243.310910.959.342.512False
3326OH96415NoNo0106.612818.12284.88724.21178.9928.0514.974.021False
3327SC79415NoNo0134.79822.90189.76816.12221.41289.9611.853.192False
3328AZ192415NoYes36156.27726.55215.512618.32279.18312.569.962.672False
3329WV68415NoNo0231.15739.29153.45513.04191.31238.619.642.593False
3330RI28510NoNo0180.810930.74288.85824.55191.9918.6414.163.812False
3331CT184510YesNo0213.810536.35159.68413.57139.21376.265.0101.352False
3332TN74415NoYes25234.411339.85265.98222.60241.47710.8613.743.700False